Senior Manager, Data Engineering

JLL

3d • Remote

About The Position

We are seeking an experienced Senior Manager, Data Engineering, to lead the Enterprise Platform Data Engineering team at JLL Technologies. In this role, you will drive people leadership, delivery excellence, and technical direction for data platforms and pipelines that power JLL's enterprise data strategy, data quality initiatives, and foundational data capabilities across the organization. You will set strategic priorities with Enterprise Platform and technology leadership, build and mentor a high-performing engineering team, and ensure reliable, governed data assets that scale with the business. This is an opportunity to shape how data engineering enables data governance, master data management, and enterprise-wide data discoverability—directly contributing to JLL's mission to reimagine real estate through technological innovation.

Requirements

• 3+ years of direct people management experience leading data engineers or equivalent technical teams, including staffing, performance management, and delivery accountability
• 10+ years of experience in data engineering and Big Data development, with proven expertise architecting and delivering enterprise-scale, fault-tolerant data platforms
• 5+ years of hands-on experience with cloud platforms (Azure or AWS), including advanced services such as Databricks, Azure Data Factory, Synapse, AWS Glue, EMR, or Redshift
• Expert-level proficiency in multiple server-side programming languages (Python, Java, Scala) with deep expertise in PySpark/Spark for distributed data processing at scale
• Proven expertise in data modeling, data architecture, and designing scalable data systems that balance performance, maintainability, and cost
• Deep understanding of machine learning lifecycle, MLOps practices, model governance, and production ML systems
• Extensive experience with diverse data technologies including SQL databases (Azure SQL, PostgreSQL), NoSQL databases (Cosmos DB, MongoDB, Cassandra), and AI-centric databases such as vector databases (Pinecone, Weaviate) and graph databases (Neo4j, Amazon Neptune)

Nice To Haves

• Master's degree in Computer Science, Engineering, Data Science, or a related field
• Deep expertise designing and implementing semantic layers, ontologies, and knowledge graphs for enterprise data systems
• Extensive experience with streaming architectures using Kafka, Spark Streaming, Flink, or similar technologies
• Expert-level understanding of DevOps principles with hands-on experience in CI/CD pipelines, infrastructure as code (Terraform, CloudFormation), and container orchestration (Kubernetes, EKS, AKS)
• Significant experience with LLM-driven workflows, RAG (Retrieval-Augmented Generation) architectures, and orchestration frameworks (LangChain, LlamaIndex, CrewAI, AutoGen)
• Experience with data governance frameworks, compliance standards (GDPR, CCPA), and enterprise security practices
• Published technical articles, conference presentations, or contributions to open-source projects in data engineering or related fields

Responsibilities

• Lead and mentor a team of data engineers, providing technical guidance, performance management, and career development while fostering a culture of excellence and continuous improvement
• Define and execute the Enterprise Platform Data Engineering roadmap, aligning team priorities with JLL's enterprise data strategy and translating business challenges into measurable technical outcomes
• Architect and oversee enterprise-scale data platforms and pipelines that consolidate siloed data sources into unified, governed, and scalable foundations for analytics, AI/ML, and real-time decision-making
• Partner with cross-functional stakeholders—including Product, Business, Data Science, and Executive Leadership—to deliver trusted data capabilities that enable advanced analytics, semantic layers, and intelligent data discovery
• Establish and drive DataOps best practices, including CI/CD pipelines, data quality frameworks, observability, lineage tracking, and compliance controls to ensure production-ready, auditable data products
• Guide technical architecture reviews, technology evaluations, and proof-of-concept initiatives to drive adoption of modern data engineering practices and emerging technologies across the organization
• Serve as the data engineering voice in executive forums, communicating roadmaps, trade-offs, and strategic direction with clarity to diverse technical and non-technical audiences

Benefits

• Discretionary bonuses
• Benefits
• Other compensation

What This Job Offers

• Job Type: Full-time
• Career Level: Senior